Abstract
This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London, and Hong Kong. By comparing whole cities’ typical patterns, we provide insights on how cultural, technological, and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity.
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Notes
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such as http://data.london.gov.uk/ for London, https://nycopendata.socrata.com/ for New York or http://www.census2011.gov.hk/ for Hong Kong.
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Acknowledgements
We thank Ericsson for providing datasets for this research and especially Dwight Witherspoon for the organizational support to the project. We also thank Christine Maynié-François for the stimulating discussions and thorough proofreading. We would further like to thank the National Science Foundation, the AT&T Foundation, the MIT SMART program, the Center for Complex Engineering Systems at KACST and MIT, Volkswagen ERL, BBVA, The Coca Cola Company, Expo 2015, Ferrovial, the Regional Municipality of Wood Buffalo, AIT, and all the members of the MIT Senseable City Lab Consortium for supporting the research.
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Grauwin, S., Sobolevsky, S., Moritz, S., Gódor, I., Ratti, C. (2015). Towards a Comparative Science of Cities: Using Mobile Traffic Records in New York, London, and Hong Kong. In: Helbich, M., Jokar Arsanjani, J., Leitner, M. (eds) Computational Approaches for Urban Environments. Geotechnologies and the Environment, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-11469-9_15
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